Name
Abstract | ||
|---|---|---|
Many stochastic model predictive control approaches use a fixed, virtual linear feedback law in the prediction to counteract noise. The feedback allows to reduce the error between a nominal prediction of the future state and its real evolution. While often overlooked, the control gain used in the feedback law is an important tuning parameter. In the stochastic case, it shapes the covariance of the closed-loop system. It thus directly influences the required constraint back-off and the influence of the noise on the cost function. Consequently, the achievable closed-loop control performance and the attainable attraction domain depend on the gain, often resulting in a trade-off between both. We propose to utilize a blending of multiple different feedback gains, which is also optimized online, to reduce the conservatism and optimize control performance. We discuss properties of the resulting optimization problem, implementation details, and properties of the closed-loop. Simulations illustrate the proposed approach and demonstrate its benefits. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.23919/ACC50511.2021.9483134 | 2021 AMERICAN CONTROL CONFERENCE (ACC) |
DocType | ISSN | Citations |
Conference | 0743-1619 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Markus J. Kögel | 1 | 0 | 0.68 |
| Rolf Findeisen | 2 | 0 | 0.68 |